[R-lang] Lmer interactions in factorial designs

[Jakke Tamminen]

My thanks to Andy, James, and Florian for their responses to my question.
The replies were, as always, prompt, helpful, and lucid. I have a couple of
quick further questions about model comparison: I think all three replies
included suggestions of doing likelihood ratio tests to assess the
significance of a single fixed factor in the model. How reliable is this? As
far as I can recall, Baayen in his book and in the JML paper only uses this
to evaluate random factors, and the paper by Bolker et al that Andy cited
recommends against it in the case of fixed factors. Are there good
alternatives? 
 
Finally, a quick follow up question regarding Florian's six-step procedure,
reproduced below. In step 5 you suggest I interpret the coefficients in the
full _or_ the reduced model. So is it acceptable to look at the coefficients
of a factor or an interaction even if the factor or interaction does not
"survive" a likelihood ratio test, i.e. does not significantly contribute to
the fit of the model?
 
I hope that makes sense, thank you again for all the help!
 
Jakke




1) l <- lmer(logRT~A*B+(1+A*B|Subject)+(1+A*B| Item), data) 
2) follow the procedure outline on our lab blog to figure out which random
effects you need:
http://hlplab.wordpress.com/2009/05/14/random-effect-should-i-stay-or-should
-i-go/
3) take the resulting model and compare it against a model without the
interaction, using anova(l, l.woInteraction).
4) if removal of the interaction is not significant, you could further
compare the model against a model with only A (see above).
5) Interpret coefficients in the full model or in the reduced model (I would
do the former unless I don't have much data or cannot reduce collinearity,
but you may prefer the latter).
6) If you find any of the scripts of references given above useful,
cite/refer to them, so that others can find them ;)   

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